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</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/jiaoxubin/2020/11/18/seq2seq-attention/" title="seq2seq&amp;attention">     <img class="post_bg" src="/jiaoxubin/img/cover/cover13.jpg" onerror="this.onerror=null;this.src='/jiaoxubin/img/404.jpg'" alt="seq2seq&amp;attention"></a></div><div class="recent-post-info"><a class="article-title" href="/jiaoxubin/2020/11/18/seq2seq-attention/" title="seq2seq&amp;attention">seq2seq&amp;attention</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2020-11-18T11:52:27.000Z" title="Created 2020-11-18 19:52:27">2020-11-18</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/jiaoxubin/tags/Thesis/">Thesis</a></span></div><div class="content"> 课程来源：https://www.bilibili.com/video/BV1aJ411F7Aq?t=2007
 编码器—解码器（seq2seq）和注意力机制
在基于词语的语言模型中，我们使用了循环神经网络。它的输入是一段不定长的序列，输出却是定长的，例如一个词语。然而，很多问题的输出也是不定长的序列。以机器翻译为例，输入是可以是英语的一段话，输出可以是法语的一段话，输入和输出皆不定长，例如

英语：They are watching.


法语：Ils regardent.

当输入输出都是不定长序列时，我们可以使用编码器—解码器（encoder-decoder）或者seq2seq。它们分别基于2014年的两个工作：

Cho et al., Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Sutskever et al., Sequence to Sequence Learning with Neural Networks

以上两个工作本质 ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/jiaoxubin/2020/11/17/%E5%85%A5%E4%BE%B5%E6%A3%80%E6%B5%8B-%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%9C%BA%E5%88%B6/" title="入侵检测+注意力机制">     <img class="post_bg" src="/jiaoxubin/img/cover/cover12.jpg" onerror="this.onerror=null;this.src='/jiaoxubin/img/404.jpg'" alt="入侵检测+注意力机制"></a></div><div class="recent-post-info"><a class="article-title" href="/jiaoxubin/2020/11/17/%E5%85%A5%E4%BE%B5%E6%A3%80%E6%B5%8B-%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%9C%BA%E5%88%B6/" title="入侵检测+注意力机制">入侵检测+注意力机制</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2020-11-17T01:10:28.000Z" title="Created 2020-11-17 09:10:28">2020-11-17</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/jiaoxubin/tags/Thesis/">Thesis</a></span></div><div class="content">
摘要

BLSTM+Attention 模型
使用卷积层捕获流量局部特征
所提出的端到端模型不使用任何特征工程技能，并且可以自动学习层次结构的关键特征


介绍

在提取某个网络流量特征的过程中，并非所有流量特征对流量分类都同等重要


相关工作

模式匹配：KMP算法、BM算法、BMH算法、BMHS算法、AC算法和AC-BM算法
传统机器学习算法
深度学习算法


拟议的工作

BAT-MC架构图

评价

模型参数
实验结果图
</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/jiaoxubin/2020/11/16/Keras%E6%B7%BB%E5%8A%A0%E7%BD%91%E7%BB%9C%E7%9A%843%E7%A7%8D%E6%96%B9%E6%B3%95/" title="Keras添加网络的3种方法">     <img class="post_bg" src="/jiaoxubin/img/cover/cover10.jpg" onerror="this.onerror=null;this.src='/jiaoxubin/img/404.jpg'" alt="Keras添加网络的3种方法"></a></div><div class="recent-post-info"><a class="article-title" href="/jiaoxubin/2020/11/16/Keras%E6%B7%BB%E5%8A%A0%E7%BD%91%E7%BB%9C%E7%9A%843%E7%A7%8D%E6%96%B9%E6%B3%95/" title="Keras添加网络的3种方法">Keras添加网络的3种方法</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2020-11-16T08:43:05.000Z" title="Created 2020-11-16 16:43:05">2020-11-16</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/jiaoxubin/tags/Thesis/">Thesis</a></span></div><div class="content"> 方法一:新建一个Sequential对象，然后使用它的add方法，一层层的去加
model = Sequential()model.add(Conv2D(20, (3, 3), input_shape=(X_train.shape[1], X_train.shape[2], X_train.shape[3])))model.add(MaxPool2D((2, 2)))model.add(Activation(&#x27;tanh&#x27;))model.add(Conv2D(30, (3, 3)))model.add(MaxPool2D((2, 2)))model.add(Activation(&#x27;tanh&#x27;))model.add(Dropout(0.5))model.add(Flatten())model.add(Dense(1000))model.add(Dropout(0.5))model.add(Dense(y_train.shape[1]))model.add(Activation(&#x27;softmax&#x27;))model.compile(o ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/jiaoxubin/2020/11/16/TCN%E6%95%B4%E7%90%86/" title="TCN整理">     <img class="post_bg" src="/jiaoxubin/img/cover/cover9.jpg" onerror="this.onerror=null;this.src='/jiaoxubin/img/404.jpg'" alt="TCN整理"></a></div><div class="recent-post-info"><a class="article-title" href="/jiaoxubin/2020/11/16/TCN%E6%95%B4%E7%90%86/" title="TCN整理">TCN整理</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2020-11-16T01:03:09.000Z" title="Created 2020-11-16 09:03:09">2020-11-16</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/jiaoxubin/tags/Thesis/">Thesis</a></span></div><div class="content"> 一、什么是TCN？
时序问题的建模大家一般习惯性的采用循环神经网络（RNN）来建模，这是因为RNN天生的循环自回归的结构是对时间序列的很好的表示。传统的卷积神经网络一般认为不太适合时序问题的建模，这主要由于其卷积核大小的限制，不能很好的抓取长时的依赖信息。 但是最近也有很多的工作显示，特定的卷积神经网络结构也可以达到很好的效果，比如Goolgle提出的用来做语音合成的wavenet，Facebook提出的用来做翻译的卷积神经网络。这就带来一个问题，用卷积来做神经网络到底是只适用于特定的领域还是一种普适的模型？ 本文就带着这个问题，将一种特殊的卷积神经网络——时序卷积网络（Temporal convolutional network， TCN）与多种RNN结构相对比，发现在多种任务上TCN都能达到甚至超过RNN模型。
 二、时序卷积神经网络
 2.1 因果卷积(Causal Convolution)
  
图：因果卷积图
因果卷积可以用上图直观表示。 即对于上一层t时刻的值，只依赖于下一层t时刻及其之前的值。和传统的卷积神经网络的不同之处在于，因果卷积不能看到未来的数据，它是单向的结构 ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/jiaoxubin/2020/10/26/SDN%E8%AF%BE%E7%A8%8B%E7%AC%94%E8%AE%B0%E4%B8%89/" title="SDN课程笔记三">     <img class="post_bg" src="/jiaoxubin/img/cover/cover8.jpg" onerror="this.onerror=null;this.src='/jiaoxubin/img/404.jpg'" alt="SDN课程笔记三"></a></div><div class="recent-post-info"><a class="article-title" href="/jiaoxubin/2020/10/26/SDN%E8%AF%BE%E7%A8%8B%E7%AC%94%E8%AE%B0%E4%B8%89/" title="SDN课程笔记三">SDN课程笔记三</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2020-10-26T10:03:17.000Z" title="Created 2020-10-26 18:03:17">2020-10-26</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox article-meta__icon"></i><a class="article-meta__categories" href="/jiaoxubin/categories/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/">计算机网络</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/jiaoxubin/tags/%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/">学习笔记</a></span></div><div class="content">课程链接：https://edu.51cto.com//center/course/lesson/index?id=76180
 SDN初认识
 1、SDN介绍

SDN的定义最重要的三句话

SDN（Software Defined Network）即软件定义网络，是一种网络设计理念，或者一种推倒重来的设计想思想。只要网络硬件可以集中式软件管理，可编程化，控制转发层面分开，则可以认为这个网络是一个SDN网络。所以说，SDN并不是一个具体的技术，不是一个具体的协议，而是一个思想、一个框架。
狭义的SDN是指“软件定义网络“，广义的SDN的概念还延伸出了：软件定义安全、软件定义存储等等。可以说，SDN是一个浪潮，席卷整个IT产业。
SDN框架主要由应用层、控制层、转发层（基础设施层）组成，具中应用层提供应用和服务（网管、安全、流控等）、控制层提供统一管理和控制（协议计算、策略下发、链路信息收集）、转发层提供硬件设备（交换机、路由器、防火墙等）进行数据转发。

</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/jiaoxubin/2020/10/18/SDN%E8%AF%BE%E7%A8%8B%E7%AC%94%E8%AE%B0/" title="SDN课程笔记一">     <img class="post_bg" src="/jiaoxubin/img/cover/cover7.jpg" onerror="this.onerror=null;this.src='/jiaoxubin/img/404.jpg'" alt="SDN课程笔记一"></a></div><div class="recent-post-info"><a class="article-title" href="/jiaoxubin/2020/10/18/SDN%E8%AF%BE%E7%A8%8B%E7%AC%94%E8%AE%B0/" title="SDN课程笔记一">SDN课程笔记一</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2020-10-18T12:31:36.000Z" title="Created 2020-10-18 20:31:36">2020-10-18</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox article-meta__icon"></i><a class="article-meta__categories" href="/jiaoxubin/categories/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/">计算机网络</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/jiaoxubin/tags/%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/">学习笔记</a></span></div><div class="content">课程链接：https://edu.51cto.com//center/course/lesson/index?id=76180
 揭秘网络的前世今生
 1、如何将两个主机连接起来？

图解：
在网络涎生之前，电脑之间都是单机运行，没有网卡、没有网线、没有协议栈，数据传输主要通过软盘、光盘等介质。
 2、网线来了

图解：

[网线+网卡+协议线]三要素，是组成“最小单元网络”的基础，缺一不可。
网线提供物理介质，承载比特流/电信号。（类似电话线承载语音流/模拟信号）。
网卡进行数据处理，例如将电脑磁盘上的数据/字节转换为网线上的电流/比特，将比特流转换为数据。
协议栈作为沟通语言，实现通信过程中的数据解析、地址寻址、流控制等。

 3、网线不够长肿么办？

图解：
终端之间如果距离太远，一旦超过网线物理传输距离上限，则数据开始丢失。
 4、中继器来了

图解：
中继器（Repeater）是一种物理层产品，能够对信息进行中继和放大，实现设备远距离传输。
 5、中继器口不够肿么办？

图解：
中继器一般只有两个接口，意味着如果网络中超过三个终端主机的话，则无法实现多主机直接的数据通信。
  ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/jiaoxubin/2020/10/14/SMOTE%E7%AE%97%E6%B3%95%E8%B0%83%E7%A0%94/" title="SMOTE算法调研">     <img class="post_bg" src="/jiaoxubin/img/cover/cover0.jfif" onerror="this.onerror=null;this.src='/jiaoxubin/img/404.jpg'" alt="SMOTE算法调研"></a></div><div class="recent-post-info"><a class="article-title" href="/jiaoxubin/2020/10/14/SMOTE%E7%AE%97%E6%B3%95%E8%B0%83%E7%A0%94/" title="SMOTE算法调研">SMOTE算法调研</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2020-10-14T07:14:15.000Z" title="Created 2020-10-14 15:14:15">2020-10-14</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox article-meta__icon"></i><a class="article-meta__categories" href="/jiaoxubin/categories/%E7%AE%97%E6%B3%95/">算法</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/jiaoxubin/tags/%E6%A0%B7%E6%9C%AC%E4%B8%8D%E5%B9%B3%E8%A1%A1/">样本不平衡</a></span></div><div class="content"> 1、SMOTE算法(2002年提出)
论文出处：http://xueshu.baidu.com/usercenter/paper/show?paperid=28300870422e64fd0ac338860cd0010a&amp;site=xueshu_se
介绍：（Synthetic Minority Oversampling Technique）合成少数类过采样技术，是在随机采样的基础上改进的一种过采样算法。实现过程如下图所示：

图一：SMOTE算法实现图
流程：图a中xix_{i}xi​是一个少数类样本，按照采样倍率N，从xix_{i}xi​的K近邻中随机选择N个样本xzix_{zi}xzi​。图a中是选择了5个K近邻样本。最后是依次在xzix_{zi}xzi​和xix_{i}xi​之间随机合成新样本，合成公式为：
xn=xi+β×(xzi−xi)x_{n}=x_{i}+\beta \times\left(x_{z i}-x_{i}\right)
xn​=xi​+β×(xzi​−xi​)
SMOTE实现简单，但其弊端也很明显，由于SMOTE对所有少数类样本一视同仁，并未考虑近 ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/jiaoxubin/2020/10/13/Markdown%E6%95%99%E7%A8%8B/" title="Markdown教程">     <img class="post_bg" src="/jiaoxubin/img/cover/cover3.svg" onerror="this.onerror=null;this.src='/jiaoxubin/img/404.jpg'" alt="Markdown教程"></a></div><div class="recent-post-info"><a class="article-title" href="/jiaoxubin/2020/10/13/Markdown%E6%95%99%E7%A8%8B/" title="Markdown教程">Markdown教程</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2020-10-13T10:57:24.000Z" title="Created 2020-10-13 18:57:24">2020-10-13</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/jiaoxubin/tags/writing/">writing</a></span></div><div class="content"> 一、Markdown可以做什么？
 1、代码高亮
@requires_authorizationdef somefunc(param1=&#x27;&#x27;, param2=0):&#x27;&#x27;&#x27;A docstring&#x27;&#x27;&#x27;if param1 &gt; param2: # interestingprint &#x27;Greater&#x27;return (param2-param1+1) or NoneClass SomeClass:pass&gt;&gt;&gt; message = &#x27;&#x27;&#x27;interpreter... prompt&#x27;&#x27;&#x27;
 2、制作代办事项To-do List

[x] 已完成项目1

[x] 已完成事项1
[x] 已完成事项2


[ ] 待办事项1
[ ] 待办事项2

 3、表格

#customers
  {
  font-family:"Trebuchet MS", Arial, Helvetica, sans-serif;
  widt ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/jiaoxubin/2020/10/13/Test-Post/" title="Test Post">     <img class="post_bg" src="/jiaoxubin/img/cover/cover2.svg" onerror="this.onerror=null;this.src='/jiaoxubin/img/404.jpg'" alt="Test Post"></a></div><div class="recent-post-info"><a class="article-title" href="/jiaoxubin/2020/10/13/Test-Post/" title="Test Post">Test Post</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2020-10-13T07:04:42.000Z" title="Created 2020-10-13 15:04:42">2020-10-13</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox article-meta__icon"></i><a class="article-meta__categories" href="/jiaoxubin/categories/web%E5%89%8D%E7%AB%AF/">web前端</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/jiaoxubin/tags/jQuery/">jQuery</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/jiaoxubin/tags/%E8%A1%A8%E6%A0%BC/">表格</a></span></div><div class="content"> 开题报告
余之入学，已迫五载。为觅真知，践科研之大道，算法为鞭，模型作马，
以窥雾计算之一斑。其大无外，科研之路，亦崎亦坦；其小无内，创新之途，
时疾时缓。博士探知之旅，有如雄关漫道。披荆棘，辨明途，成文绝非一蹴。
励我勤，拨云雾，感念贵人相助。
“
饮其流者怀其源，学其成时念吾师。”首谢邬贺铨院士与周贤伟教授。
邬院士克明峻德，常启我明思，指我迷津，解我所惑，励我勤勉。正所谓“片
言可以明百意，坐驰可以设万景”，心切怅惘之时，邬院士以其纵观行业先知
之识，几席长谈，为我指明研究之方向。周老师格物致知，常躬亲身授，传
我法门。教我“博观而约取，厚积而薄发”；令我明科研之理，悟学问之道。
周老师常言“看好一本书”，一语道破科研之奥妙，乃是在精而不在多，在深
而不在广也。有感是，五载时光转瞬之间，尊师教诲铭记心田。而今起航另
扬风帆，复以师训攻坚克难。
“学贵得师，亦贵得友。”再谢北科大计算机与通信工程学院诸位师长：
王建萍教授、陈月云教授、吕兴教授、林福宏副教授、安建伟副教授、许海
涛副教授及通信系其他师长亦师亦友，助我良多。正所谓“新竹高于旧竹枝，
全凭老干为扶持。”科研途中，诸位老 ...</div></div></div><nav id="pagination"><div class="pagination"><a class="extend prev" rel="prev" href="/jiaoxubin/"><i class="fas fa-chevron-left fa-fw"></i></a><a class="page-number" href="/jiaoxubin/">1</a><span class="page-number current">2</span></div></nav></div><div class="aside_content" id="aside_content"><div class="card-widget card-info"><div class="card-content"><div class="card-info-avatar is-center"><img class="avatar-img" src="/jiaoxubin/img/avatar.gif" onerror="this.onerror=null;this.src='/jiaoxubin/img/friend_404.gif'" alt="avatar"/><div class="author-info__name">Xubin Jiao</div><div class="author-info__description"></div></div><div class="card-info-data"><div class="card-info-data-item is-center"><a href="/jiaoxubin/archives/"><div class="headline">Articles</div><div class="length-num">20</div></a></div><div class="card-info-data-item is-center"><a href="/jiaoxubin/tags/"><div class="headline">Tags</div><div class="length-num">13</div></a></div><div class="card-info-data-item is-center"><a href="/jiaoxubin/categories/"><div class="headline">Categories</div><div class="length-num">7</div></a></div></div><a class="button--animated" id="card-info-btn" target="_blank" rel="noopener" href="https://github.com/xxxxxx"><i class="fab fa-github"></i><span>Follow Me</span></a><div class="card-info-social-icons is-center"><a class="social-icon" href="https://github.com/xxxxx" target="_blank" title="Github"><i class="fab fa-github"></i></a><a class="social-icon" href="mailto:2356692289@qq.com" target="_blank" title="Email"><i class="fas fa-envelope"></i></a></div></div></div><div class="card-widget card-announcement"><div class="card-content"><div class="item-headline"><i class="fas fa-bullhorn card-announcement-animation"></i><span>Announcement</span></div><div class="announcement_content">This is my Blog</div></div></div><div class="sticky_layout"><div class="card-widget card-recent-post"><div class="card-content"><div class="item-headline"><i class="fas fa-history"></i><span>Recent Post</span></div><div class="aside-list"><div class="aside-list-item"><a class="thumbnail" href="/jiaoxubin/2021/01/15/ED-coder/" title="ED-coder"><img src="/jiaoxubin/img/cover/cover19.jpeg" onerror="this.onerror=null;this.src='/jiaoxubin/img/404.jpg'" alt="ED-coder"/></a><div class="content"><a class="title" href="/jiaoxubin/2021/01/15/ED-coder/" title="ED-coder">ED-coder</a><time datetime="2021-01-15T02:46:29.000Z" title="Created 2021-01-15 10:46:29">2021-01-15</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/jiaoxubin/2021/01/14/%E5%AD%A6%E4%B9%A0%E8%B5%84%E6%BA%90/" title="学习资源"><img src="/jiaoxubin/img/cover/cover18.jpeg" onerror="this.onerror=null;this.src='/jiaoxubin/img/404.jpg'" alt="学习资源"/></a><div class="content"><a class="title" href="/jiaoxubin/2021/01/14/%E5%AD%A6%E4%B9%A0%E8%B5%84%E6%BA%90/" title="学习资源">学习资源</a><time datetime="2021-01-14T08:24:43.000Z" title="Created 2021-01-14 16:24:43">2021-01-14</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/jiaoxubin/2021/01/14/Code1/" title="Code1"><img src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1/img/default.jpg" onerror="this.onerror=null;this.src='/jiaoxubin/img/404.jpg'" alt="Code1"/></a><div class="content"><a class="title" href="/jiaoxubin/2021/01/14/Code1/" title="Code1">Code1</a><time datetime="2021-01-14T06:27:49.000Z" title="Created 2021-01-14 14:27:49">2021-01-14</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/jiaoxubin/2021/01/13/WSN-1/" title="WSN-1"><img src="/jiaoxubin/img/cover/cover17.jpeg" onerror="this.onerror=null;this.src='/jiaoxubin/img/404.jpg'" alt="WSN-1"/></a><div class="content"><a class="title" href="/jiaoxubin/2021/01/13/WSN-1/" title="WSN-1">WSN-1</a><time datetime="2021-01-13T05:36:45.000Z" title="Created 2021-01-13 13:36:45">2021-01-13</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/jiaoxubin/2021/01/13/Paper2/" title="Paper2"><img src="/jiaoxubin/img/cover/cover16.jpeg" onerror="this.onerror=null;this.src='/jiaoxubin/img/404.jpg'" alt="Paper2"/></a><div class="content"><a class="title" href="/jiaoxubin/2021/01/13/Paper2/" title="Paper2">Paper2</a><time datetime="2021-01-13T01:32:21.000Z" title="Created 2021-01-13 09:32:21">2021-01-13</time></div></div></div></div></div><div class="card-widget card-categories"><div class="card-content"><div class="item-headline"><i class="fas fa-folder-open"></i><span>Categories</span></div><ul class="card-category-list" id="aside-cat-list">
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